Change point analysis has a long tradition in statistics dating back to 1950s (see [1]})
and has been a very active field of research ever since due to its importance in time series analysis,
signal processing and many other applications
where data is routinely collected over time in naturally nonstationary environments.
Research in change point analysis has predominantly focused on the following two directions:
One line of research is on the development of sequential or online methodologies based on optimal stopping (see e.g. the recent books by [2]} and [3]})
which is being actively studied to date but will not be part of this review.
The second direction addresses the problem of change point detection in a posteriori or offline settings,
and much of the research in the last century dealt with change point testing
against the at-most-one-change (AMOC) alternative
(see e.g. the books by [4]} and [5]}).
Based on such tests, the location of a single change point can be estimated
with the optimal localisation properties,
typically by scanning for the point where the corresponding statistic attains its maximum.
More recent papers tackle change point problems in more complex situations
(see the review papers by [6]} and [7]}
and also Section  below),
both sequentially and retrospectively,
and this review paper focuses on the latter.
